{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "from sklearn.metrics import (accuracy_score, f1_score, precision_score, recall_score)\n",
    "from utils.data import *\n",
    "from utils.deep_learning import *\n",
    "from utils.measuring_performance import *\n",
    "from utils.misc import *\n",
    "from deepctr.inputs import DenseFeat, SparseFeat, get_feature_names\n",
    "from deepctr.layers import custom_objects\n",
    "from deepctr.models import DeepFM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_metrics_per_epoch(history, smoothing=False):\n",
    "    metrics_per_epoch = pd.DataFrame(history)\n",
    "    \n",
    "    if smoothing:\n",
    "        losses = metrics_per_epoch['loss'].rolling(window=10).mean()\n",
    "        val_losses = metrics_per_epoch['val_loss'].rolling(window=10).mean()\n",
    "        aucs = metrics_per_epoch['auc'].rolling(window=10).mean()\n",
    "        val_aucs = metrics_per_epoch['val_auc'].rolling(window=10).mean()\n",
    "        \n",
    "    else:\n",
    "        losses = metrics_per_epoch['loss']\n",
    "        val_losses = metrics_per_epoch['val_loss']\n",
    "        aucs = metrics_per_epoch['auc']\n",
    "        val_aucs = metrics_per_epoch['val_auc']\n",
    "    \n",
    "    fig = plt.figure(figsize=(10, 4))\n",
    "\n",
    "    ax1 = plt.subplot(1, 2, 1)\n",
    "    _ = ax1.plot(losses, linewidth=1.2, label='Training Loss')\n",
    "    _ = ax1.plot(val_losses, linestyle='--', linewidth=1.2, label='Validation Loss')\n",
    "    _ = ax1.set_title('Loss per Epoch')\n",
    "    _ = ax1.legend(loc='best')\n",
    "        \n",
    "    ax2 = plt.subplot(1, 2, 2)\n",
    "    _ = ax2.plot(aucs, linewidth=1.2, label='Training AUC')\n",
    "    _ = ax2.plot(val_aucs, linestyle='--', linewidth=1.2, label='Validation AUC')\n",
    "    _ = ax2.set_title('AUC per Epoch')\n",
    "    _ = ax2.legend(loc='best')\n",
    "    \n",
    "    return metrics_per_epoch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_DIR = os.path.abspath('../../Data/display_advertising_challenge/processed')\n",
    "MODEL_DIR = os.path.abspath('models')\n",
    "USE_TFRECORD = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.exists(MODEL_DIR):\n",
    "    os.makedirs(MODEL_DIR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset_type = 'train+valid'\n",
    "test_dataset_type = 'test'\n",
    "model_type = 'deepfm'\n",
    "model_path = os.path.join(MODEL_DIR, '_'.join([model_type, 'model', train_dataset_type]))\n",
    "score_path = os.path.join(MODEL_DIR, '_'.join([model_type, 'score', test_dataset_type]) + '.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_name, num_feature_names, cat_feature_names, n_categories = load_pickle(\n",
    "    os.path.join(DATA_DIR, '_'.join([train_dataset_type, 'metadata.pkl'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "if USE_TFRECORD:\n",
    "    train_dataset_path = os.path.join(DATA_DIR, '_'.join(['dataset', train_dataset_type]) + '.tfrecord')\n",
    "    test_dataset_path = os.path.join(DATA_DIR, '_'.join(['dataset', test_dataset_type]) + '.tfrecord')\n",
    "    \n",
    "    shuffle_buffer_size = 2 ** 20\n",
    "    train_dataset = extract_dataset(train_dataset_path, compression_type='GZIP', \n",
    "                                    shuffle_buffer_size=shuffle_buffer_size, is_training=True)\n",
    "    test_dataset = extract_dataset(test_dataset_path, compression_type='GZIP', \n",
    "                                    shuffle_buffer_size=shuffle_buffer_size, is_training=True)\n",
    "    \n",
    "    n, m = 36672494, 9168123\n",
    "    # n = get_n_examples(train_dataset)\n",
    "    # m = get_n_examples(test_dataset)\n",
    "    \n",
    "else:\n",
    "    df_y_train = pd.read_pickle(os.path.join(DATA_DIR, '_'.join(['df', 'y', train_dataset_type]) + '.pkl'))\n",
    "    df_X_train = pd.read_pickle(os.path.join(DATA_DIR, '_'.join(['df', 'X', train_dataset_type]) + '.pkl'))\n",
    "    df_y_test = pd.read_pickle(os.path.join(DATA_DIR, '_'.join(['df', 'y', test_dataset_type]) + '.pkl'))\n",
    "    df_X_test = pd.read_pickle(os.path.join(DATA_DIR, '_'.join(['df', 'X', test_dataset_type]) + '.pkl'))\n",
    "\n",
    "    train_model_input = {column: df_X_train[column].values for column in df_X_train.columns}\n",
    "    test_model_input = {column: df_X_test[column].values for column in df_X_test.columns}\n",
    "    \n",
    "    n = df_y_train.shape[0]\n",
    "    m = df_y_test.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding_dim = 4\n",
    "num_features = [DenseFeat(feature, 1) for feature in num_feature_names]\n",
    "cat_features = [SparseFeat(feature, vocabulary_size=n_categories[feature], \n",
    "                           embedding_dim=embedding_dim, use_hash=False) for feature in cat_feature_names]\n",
    "linear_features = num_features + cat_features\n",
    "dnn_features = num_features + cat_features\n",
    "all_feature_names = get_feature_names(num_features + cat_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = DeepFM(linear_features, dnn_features, task='binary')\n",
    "\n",
    "if len(get_available_gpus()) > 1:\n",
    "    model = tf.keras.utils.multi_gpu_model(model, gpus=n_gpus)\n",
    "    \n",
    "model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=[tf.keras.metrics.AUC()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "callbacks = [tf.keras.callbacks.EarlyStopping(monitor='val_auc', patience=3, mode='max'),\n",
    "             tf.keras.callbacks.ModelCheckpoint(\n",
    "                 filepath=model_path + '.h5', monitor='val_auc', save_best_only=True)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs = 300\n",
    "batch_size = 2 ** 17"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "if USE_TFRECORD:\n",
    "    steps_per_epoch = n // batch_size\n",
    "    validation_steps = m // batch_size\n",
    "    \n",
    "    train_dataset = transform_dataset(train_dataset, num_feature_names, cat_feature_names, \n",
    "                                      target_name=target_name)\n",
    "    test_dataset = transform_dataset(test_dataset, num_feature_names, cat_feature_names, \n",
    "                                     target_name=target_name)\n",
    "    \n",
    "    train_generator = load_dataset(train_dataset, batch_size=batch_size, is_training=True)\n",
    "    valid_generator = load_dataset(test_dataset, batch_size=batch_size, is_training=True)\n",
    "    \n",
    "    history = model.fit_generator(train_generator, steps_per_epoch=steps_per_epoch, epochs=epochs, \n",
    "                                  verbose=False, validation_data=valid_generator, \n",
    "                                  validation_steps=validation_steps, callbacks=callbacks)\n",
    "    \n",
    "else:\n",
    "    history = model.fit(train_model_input, df_y_train.values, batch_size=batch_size, epochs=epochs, \n",
    "                        verbose=True, validation_data=(test_model_input, df_y_test.values), callbacks=callbacks)\n",
    "    \n",
    "history = history.history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(model_path + '.h5')\n",
    "dump_pickle(model_path + '_history.pkl', history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/a406127/anaconda3/envs/ctr_prediction/lib/python3.7/site-packages/tensorflow_core/python/framework/indexed_slices.py:424: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n",
      "  \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\n",
      "/Users/a406127/anaconda3/envs/ctr_prediction/lib/python3.7/site-packages/tensorflow_core/python/framework/indexed_slices.py:424: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n",
      "  \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\n"
     ]
    }
   ],
   "source": [
    "model = tf.keras.models.load_model(model_path + '.h5', custom_objects=custom_objects)\n",
    "history = load_pickle(model_path + '_history.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "metrics_per_epoch = show_metrics_per_epoch(history, smoothing=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "if USE_TFRECORD:\n",
    "    test_dataset = extract_dataset(test_dataset_path, compression_type='GZIP', \n",
    "                                   shuffle_buffer_size=shuffle_buffer_size, is_training=False)\n",
    "    test_dataset = transform_dataset(test_dataset, num_feature_names, cat_feature_names, \n",
    "                                     target_name=target_name)\n",
    "    test_generator = load_dataset(test_dataset, batch_size=batch_size, is_training=False)\n",
    "    \n",
    "    y_true = get_target(test_generator)\n",
    "    y_score = model.predict_generator(test_generator).ravel()\n",
    "    \n",
    "else:\n",
    "    y_true = df_y_test.values\n",
    "    y_score = model.predict(test_model_input).ravel()\n",
    "\n",
    "dump_pickle(score_path, y_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wYEy14+L+PiJjoob1ERljPGd9RMYYz1lGZIzxXJg6q0UkAZgICBAErsc5158ECoAc4GpV3Soi/YABQD4wWlXfF5EawDSgPLAJ6Kuq2aUtW1yd4zcXNCbaJCSEvhTvfABV7QCMAMYA44FBqtoFmA4MFZE6wM1AB+BcYKyIJAN3AdNUtROwHBgQprJFskBkTLQI053Vqvou0N99Wx/YBVyhqt+46xKBfUBbYJGq5qhqBrAWaI5z281HbtlZwNlhKlska5oZEy3C2Eekqvki8m/gIqCnqm4GEJH2wEDgTJxsJaPQZplAZZx7/zKKWXekZYtkGZEx0cLvD30JgapeAzQBJopIqohcDjwL9FDVbTg3GqcV2iQNJ3sqvP5Q6460bJEsIzImSgTDlBGJyFXA8ao6FsgGAsDFOM21Lqr6h1t0KTBGRFKAZKAZ8C2wCGdI1hSgG7AgTGWLZBmRMdEifKPvpwOtRORzYDYwGKezOg2YLiLzROQeVd0CPIETPD4FhqvqPmA0cIWILALaARPCVLboQ7chHvHJhnhEXkmHeGTPfy3kk7FC5yti6qajwzbNRKQa0FpV54rI/wGtgbtVdXXEa2dMPInjQa+hHPmrQFMRORu4FHgPp8PLGBNOPl/oS4wJJRBVVdUJwIXAFFV9GagQ2WoZE4fCfNXsaBLKVTO/iJyKM79QZxFpGeJ2xpgSCNdVs6NRKKF1KPAw8Iiqrsdplt0S0VoZE49szuqiqeonIrJQVXNEpBHOJPixN5WgMR4L+u1xQkUSkZFAYxEZAXwOfIfTTOsX4boZE1eCMZjphCqUI78QJ+j0AqaqalegVURrZUw8sqtmxUpQ1RzgPOBD92GJqZGtljFxyPqIivWJiHyLM2blc5z+oZkRrZUxcciumhVDVW/DGdTWzn2G/SBVjdLnsRhz9Ar6EkJeYk0ondWC85yyiiLiAxJEpKGqnhnx2hkTR6yzuniv48wl0gr4BqjFYYb0G2OOQBz3EYVyRH5VvRtnOshlOJfuT49orYyJQ0GfL+Ql1oQSiLLdia9/AE51r6ClRLZaxsSfoM8f8hJrQrlqNhXnKtmVwGIR+RvwW0RrZUw8isFMJ1ShXDWbAFziznHbBXgep3lmjAmjgC8h5CXWFJkRichdB70v/DYduDdCdTImLsVikytUxTXN4jdPNMYLcdw0KzIQqeo94Dy+VlUL3Nc13SaaMSbMgnH8LIsij1xEqovIfKBnodXPisjn7jzWxpgwssv3hzYe596hNwut6wl8AoyLZKWMiUd2+f7Q0lW1d+EVqhoE7nEHwRpjwigWr4aF6kjnni4Iay2MMTHZ5ApVcTneBhHpfvBK94ZG67A2JsyC+EJeYk1xGdEdwKciMhtYgnM5/zScKUG6lUHdjIkrsdj3E6oij1xVFWgD/IozO2M3YAPQUlW/KZPaGRNHLCMqgqpuBu4qrowxJjyss9oY47l4bpr5gsGg13U4lKislDFHIOR21Pp160L+7/6EE0+MqfZZ1GZEHc+3ZzhG0sKZnZm20OJ9JPXqWLJYEc+X74sbfR/gv5nJwb9QUFXjt0FrTAQEgxaI/oeqxm+D1RgPxPOg11Ce4lELZ3bGijiZUQLQUFWvjnDdjIkrgTgORKEc+XSgJdAb5wmvFwCBSFbKmHhk9xEVr4aqdhSRR3CC0v3A3MhWy5j4E64AIyJJwItAAyAZGK2q77mf9cJ5SGo7930/YACQ75Z7X0RqANOA8sAmoK+qZpe2bHF1DiUj2un+VaCFqmYASSH9IsaYkAWDvpCXw+gN7FDVTsDfgAkAItIK+CfuxScRqQPcDHQAzgXGuk/suQuY5m6/HBgQprJFCiUQfSoibwJzgCEi8iywL4TtjDElEMam2ZvASPe1D8gXkeo4rZnBhcq1BRapao6bYKwFmgMdceYiA5gFnB2mskUK5Skew4Fhqvoz8A+czOiiw21njCmZcAUiVd2jqpkikga8hROUXgBuBTILFa0EZBR6nwlUPmj9odYdadkihXLV7Gr3bwd31Q6gK/DS4bY1xoQuEAzfVTMRqQu8AzwN/Ag0Bp7BeTjqSSIyDvgUSCu0WRrO4+V3u6/3HmJdacoWKZTO6r8Uep0EdAI+xwKRMWEVCF9ndW2crpSBqvqJu/pk97MGwGuqOtjtyxkjIik4ndrNgG+BRTjT/UzBmXVjAbA0DGWLdNhApKp9DzrIasDrh9vOGFMyYbwsfydQFRgpIvv7irqp6t7ChVR1i4g8gRM8/MBwVd0nIqOBf7tXvrYDvVQ1q7Rli6twiQe9ikg54DtVbVyiDUsmaGPNIsvGmkWeO9Ys5Oiy7IcdIf+DtG5SPaZuJgqlj+gz/jzm7ATgw0hWyph4FIs3KoYqlD6iUYVeB4Htqro6MtUxJn6Fs7P6aBNKIOqpqoMKrxCRf6vqNRGqkzFxyUbfH4KITMJphrURkZMLfZTEYe4JMMaUXDwP4CwuIxqNM1ZlPE7zbH+4zgfWRLRWxsSheM6IinuKxwZVnYdzC3e6qs7HuVX7XGyIhzFhF8+j70PpHXsFOMZ9nelu83LEamRMnArjoNejTiid1fVV9QIAVd0NjBARe66ZMWFWEIMBJlShZERBEUnf/0ZEmgJ5kauSMfEpnptmoWREtwEfi8iv7vuaOPOdGGPCKBabXKEKZRqQuUA94AbgPZxZ2GZFuF7GxJ1gMPQl1oQyxKMhzpSPfYEqwBiceauNMWEUrtH3R6Pibmi8CLgeaI0zr0lvYKKq3ltGdTMmrgQCFogO5W2cKSfbqepaOPDQRWNMBMRiJ3SoigtEzYE+wEIR2QC8epjyxphSCMRg30+oiruz+ltVvQ04DhgLdAFqi8gHItK9jOpnTNywGxqLoaoFwAxghojUBK7CCUw2J5ExYRSLV8NCVaKmlqpuAx5zF2NMGNlVM2OM5+yqmTHGc/HcWW2ByJgoYX1ExhjP2X1ExhjPWdPMGOM5a5oZYzxXYFfNjDFes4zIGOM5C0QGgBfGtSY7uwCATVv3MeezrfS7qiH5+QF2ZuQx+vHvyckJ8K9+J5LerDJ79xXwzJT1rP4hkyqVkxg6sAlpFRPx+32Mfvx7Nm1xHnbi88HDd6ez4MvtzPhoM2kVE7lrSFNSKySSsTuPByf8wK6M2J5999f1K5j71iP0ueNltmxcw/svj8LvT6B67QZc0Gc0Pr+fxXOm8N1SZ+RQo/Qz6XLhQPZlZ/LOpNvJ2buHgvw8zrl8GHUbtQIgECjgrWdvoXWnS2mU3unAvv7Y+jOvPzWQG+6dCUB25k7efn4I+Xk5pFWpxYV97ycpuXzZ/wiHYZ3VhnJJPnzAoDtXHFg37ZnTuOn/vmHnrjwGXN2Q8885hk1b9lLvuAr0G7KMSmmJPDqqOdfduowb+57Ax/N/59OF22iVXoX6x1c4EIj69W5IWup/f+qrL6vHytW7efnNjbRpUYUBVzfkwSd/KOtDLjOLZk1i5eIZJCVXAGD+e0/R+fwbady8M9Ofv40fVs6j1nGNWfXlTK4b8QY+n5/JD/SiWeuurP56Ng2bteOMrtewfct63n7uNgbcPZ0/ft/Iuy8MZfcfW2nd6dID+1rxxQyWzH2JrMydB9bNn/kU6aefR8uOF7Pww+f5av7rtDunT1n/DIcVi4NZQxW/D9s+SKOGFUlJTuCxe9MZP7o5J0sag+5cwc5dTqaSkOAjNzdAg7qpLFn+B8EgZOzOJxAIUq1KEunNKlGzejLj7mvOOV1qsXzVLgC6tK9BMBhkybI/DuyrQd0KfPm1837lmt00Pym2H5xbtWZdLrvpyQPv69Rrxt6sDILBIDn7skhISKJS1Tr0vmUSfn8CPp+PgoJ8EpPK0e6cPpza+XIAAgUFJCaVAyA3J5vzrxlNg6Zt/7Sv8qmV6DP0z0+72vjjsgMZU6P0M/lp9eJIHu4RKwiEvsQaC0SufTkBXn3nF269axWPPP0jdw1pxq6MXADObFeD1ulV+OjTLfz40x5Ob12NhAQfx9ZOoUG9VFJSEjimVgqZe/IYPHIlW7flcGXPejSsV4GunWsx6ZUNf9rXj+uz6Ni2OgAd21YnJTm2/xlOanMuCQn/zQir1a7PrGljeGpEd7J276BB07YkJCZRIa0qwWCQOa8/SJ16zahepyEpFSqRVC6FPRnbeGfiHZx1ya0A1KnblJrHnvg/+2rS4i+UczOv/XL27SG5fBoAySmp7NubGcGjPXI2Z7Xhl9+y+XXzXuf1pr1kZOZRvVoyXTrUoEv7mgwZtZLcvCD/Wb6TZo3TePL+FmzYmIWuy2R3Zh4ZmfksXLoDgEVLd9D/qgaUS/JTs3oyT4xpQZ1aKeTnB9jy+z5efmsjg/s3YsLYFiz+6g+2bsvx8tDL3Eev3k/fYVOpdVxjln76CrNff5Aeve8iPy+HGZPvpFxKKj16332g/NZflbefG0LXy+6ggbQt5psPLTmlIrn7skgql0LOvixSKqSF83DCJhYDTKgiEohE5DMg+aDVPiCoqu0jsc/S6tG1Dic2SOXRZ9ZSvVo5Uisk0uPsOjRqmMrgkSvJzXXy4brHluf3bTncOPQbatVIZsQtTdmTVcDK1Rm0a1ON2Z/9TsuTK/PTxmyembL+wPdf+4/67NiZy5JlO2nXphozZ2/m2+9307l9DVat2e3VYXuifGplkstXBCCtSi1+WbuMYDDIa0/eSIOmZ9Cxe78DZbdtWsubzwym5/WPU6du0yPaX91Grfhx5XxadryYtas+p17jNmE5jnCzzurwGwZMBC4C8iO0j7B6/+MtDB8sPP1gS4JBePipH3j83ub8sG4Pj45yni/5yYLf+XDuFgZc05CLuh9Lbl6Ax579EYAJL6xj2CDh792OJSu7gHseXlPkvjb+ls2IW5yTavuOXMY+oZE/wChyQZ/RvP3crfj9CSQkluP8a+7l++Vz2aD/IT8/l7Xffg7AWRffyqJZE8nPy+GjV8cAkFI+jSsGPV2i/Z153g28++Iwli14kwoVq3Jx/0fCfkzhEM8ZkS8YoaMXkduBtar6zhFsHux4/vxwV8kUsnBmZ6YtjOP/8stAr44+IPSRrM/NIeR/kAHnxNYI2Yj1Eanqw5H6bmNiUSDMV8NE5HTgQVXtIiK1cFopVYEE4GpVXSci/XCeW5gPjFbV90WkBjANKI/zQNW+qppd2rLF1TW2L9cYcxQJBENfDkdE7gAmASnuqoeAV1T1TGAE0FRE6gA3Ax2Ac4GxIpIM3AVMU9VOwHJgQJjKFskCkTFRIhgMhryEYB1wcaH3HYDjRWQucCUwD2gLLFLVHFXNANbiPEasI/CRu90s4OwwlS2SBSJjokQ47yNS1beBwuOGGgA7VfVsYCMwFKgEZBQqkwlUPmj9odYdadkiWSAyJkoEAqEvR2AH8J77eibQBtgNFL6pKg3YddD6Q6070rJFskBkTJSI8J3VC4H9D0Y9E/gOWAp0EpEUEakMNAO+BRYVKtsNWBCmskWyQGRMlIjwWLMhwNUi8gXwN+B+Vd0CPIETPD4FhqvqPmA0cIWILALaARPCVLZIEbuPqJTsPqIIs/uIIq+k9xE9Mj30e6tvu9hv9xEZY8LPhngYYzwXnY2TsmGByJgoEYjjlMgCkTFRItxDPI4mFoiMiRKBOG6bWSAyJkoELSMyxngtSm+lKRMWiIyJEtZHZIzxnGVExhjPFRRYIDLGeCyOEyILRMZEC7uh0RjjOesjMsZ4zu4jMsZ4zu6sNsZ4ruAIZzyLBRaIjIkScZwQWSAyJloE7aqZMcZr1kdkjPGcZUTGGM/ZEA9jjOcsIzLGeM7urDbGeM7GmhljPGcZkTHGc9ZHZIzxnA3xMMZ4zjIiY4znrI/IGOM5u2pmjPGcNc2MMZ6zplkUWjizs9dViHm9Ovq8roIppCC/wOsqeCZaA5GdISbuWEZkjPGc9REZYzwXrkAkIknAv4EGQAHQD8gHpgBB4FvgJlUNiMjdQA/388GqulREGpW2bEnr7D/iozXGhFUgGAh5OYzuQKKqtgfuBcYAjwEjVLUTTtfHhSLSGugMnA5cATzlbl+qskdy7JYRGRMlAvlhG+LxA5AoIn6gEpAHnAHMdz+fBZwDKDBHVYPARhFJFJGawKmlLPtOSStsga9Opt4AAAVzSURBVMiYKBHGzuo9OM2y74EawHnAmW4QAcgEKuMEqR2Fttu/3lfKsiVmTTNjokQgEAh5OYxbgNmq2gRogdNfVK7Q52nALmC3+/rg9YFSli0xC0SlICJ+EXlWRBaLyDy3485EgIicLiLzvK5HJAUDwZCXw9gJZLiv/wCSgOUi0sVd1w1YACwCznX/O64H+FV1exjKlpg1zUrn70CKqrYTkTOARznCzjpTNBG5A7gKyPK6LpEUPHwndKgeB14UkQU4mdCdwFfARBEpB6wB3lLVArfMYpyk5CZ3+yGlKXskFfbF801UpSUijwFLVfU19/1vqnqcx9WKOSJyCbASeFlVz/C6PpHS/dpVIZ+MH76YHlM3/VrTrHQq8d8UGKBARCzLDDNVfRvnyk9MKygoCHmJNXbSlM7BHXh+Vc33qjLm6BbPd1ZbRlQ6i3BuHsPtI1rlbXXM0SwYCIS8xBrLiErnHaCriHyBc1dpX4/rY45i8ZwRWWe1MVHirCuWhnwyfvJa25jqrLaMyJgoYfMRGWM8F89NMwtExkSJMN7QeNSxQGRMlIjnjMg6q40xnrOMKMJEpAHO/DCrcWaxKwdsAvqq6q9H+J19gC6q2kdEPgSuU9VNRZS9B5irqiEPRhSRoKr+z1UZERHgYaChu2oVcLOqbheRUQCqOqokx2IM2A2NZWWTqrZU1VaqejLOAMQnw/HFqtq9qCDk6gwklHY/InIs8BkwUVXTgeY4U4OWeBIsYw5mGZE3PgcuABCRDcASoCXQCfgbMBjnfxJf48wBvE9ErgJG4Awr+Rln8qv923cBtuBM39kRZ1zWfUAy0AaYJCIXAXuBZ4DqQDYwSFWXu1nbVKAi8GURdb4BZ4a+mQCqGhSRB4GfDh5fJyIDcUbLp+LMV3O5qq4RkUeArjjzKM9Q1XtE5CzgIZxscSfwD3d6CRNHLCMqY+7E5pfjDA/Zb5aqClATZ6Lz9qraEvgduM3NRh4CzgTa8efxbfsNwgkkzYCzgbuA13Cyr+tUdRXOBFl3qGproL/7OcAEYIq7z0UHf7GrFU7APEBVC1T11cLj60SkEs70KF1U9RTgXeBGEakPdFPVFkB7oLGIpOAE1+tVtQ0wE2hd9K9nYpVlRGXjWBH5xn2dDCwFhhX6fP8J/hegMfCl0x1DOWAZzon7hapuBRCRqcBZB+2jM/C8+wSFLcDJblncvxWB04DJ+9cBFUWkOk5G9Q933SvAC4c4hgAhPG9OVXeLSC/gChFpgpPhfQP8BuwVkUXA+zgTru8TkfeAd0TkXZws6ePD7cPEHgtEZWOTm20UZa/7NwF4Q1VvhgPBIxEn6BTOXg81wv9P02S4s0VuLLQqAdhXuB4icjzODH7BQt8f5M/Tf+73FU4zr/A+/DgTYd1QaF1dYB5OljULJyi2UtV8ETkdJ2B2BxaLSGdVfVxEZuLMq/yQiLylqmMOsX8Tw6xpFl3mAReJSC0R8eH05wwGFgJniMhx7sl/+SG2/Ry4TER8IlIL58kKyThBK1FVM4AfRaQ3gIh0dbcBmAv0dl9f7G53sOeBHiKyf7YBHzASqLU/U3OdBqxV1cdxMr1uQIKItHLr9Lmq3oZzFVFEZAmQpqrjcGYWtKZZHLJAFEVUdQVwD/Ap8B3Ov88D7ok+CCdgLMXpsD7Y0zhTqa5wyw1S1UzgI+BZEWkPXAlcJyIrgbE4nchBYCBwibu+O87TGA6u2xacoDJERFa59WuM0x9U2BzALyKrcTq+NwANVXU5zjSj34rIMnf9LJxpTKeIyNc4/VZ3l+Q3M7HBbmg0xnjOMiJjjOcsEBljPGeByBjjOQtExhjPWSAyxnjOApExxnMWiIwxnrNAZIzx3P8D8GdBQnnIA3cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 288x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ctr = y_true.mean()\n",
    "y_pred = get_y_pred(y_score, threshold=ctr)\n",
    "\n",
    "norm_entropy = get_norm_entropy(y_true, y_score)\n",
    "calibration = y_score.mean() / ctr\n",
    "accuracy, precision, recall, f1 = accuracy_score(y_true, y_pred), precision_score(y_true, y_pred), \\\n",
    "    recall_score(y_true, y_pred), f1_score(y_true, y_pred)\n",
    "\n",
    "confusion_matrix = plot_confusion_matrix(y_true, y_pred)\n",
    "auroc = plot_roc_curve(y_true, y_score)\n",
    "auprc = plot_pr_curve(y_true, y_score)\n",
    "_ = plot_lift_curve(y_true, y_score)\n",
    "_ = plot_class_density(y_true, y_score, threshold=ctr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "dump_pickle(os.path.join(MODEL_DIR, '_'.join([model_type, 'metric', train_dataset_type]) + '.pkl'), \n",
    "            (norm_entropy, calibration, accuracy, precision, recall, f1, confusion_matrix, auroc, auprc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_dataset_type = 'quiz'\n",
    "score_path = os.path.join(MODEL_DIR, '_'.join([model_type, 'score', test_dataset_type]) + '.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "if USE_TFRECORD:\n",
    "    test_dataset_path = os.path.join(DATA_DIR, '_'.join([model_type, 'dataset', test_dataset_type]) + '.tfrecord')\n",
    "    test_dataset = extract_dataset(test_dataset_path, compression_type='GZIP', \n",
    "                                    shuffle_buffer_size=shuffle_buffer_size, is_training=False)\n",
    "    test_dataset = transform_dataset(test_dataset, num_feature_names, cat_feature_names, target_name=target_name) \n",
    "    test_generator = load_dataset(test_dataset, batch_size=batch_size, is_training=False)\n",
    "    \n",
    "    y_score = model.predict_generator(test_generator).ravel()\n",
    "    \n",
    "else:\n",
    "    df_X_test = pd.read_pickle(os.path.join(DATA_DIR, '_'.join(['df', 'X', test_dataset_type]) + '.pkl'))\n",
    "    test_model_input = {column: df_X_test[column].values for column in df_X_test.columns}\n",
    "    \n",
    "    y_score = model.predict(test_model_input).ravel()\n",
    "    \n",
    "dump_pickle(score_path, y_score)"
   ]
  }
 ],
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